How to scrape YouTube transcripts at scale for RAG pipelines using Python

Developers building retrieval-augmented generation (RAG) pipelines over video content rely on transcripts as the primary data payload, since titles and descriptions lack sufficient detail. YouTube's official Data API v3 cannot download captions for third-party videos, making it a dead end for most RAG use cases. A practical workaround involves accessing the public player surface, which exposes caption track metadata without requiring OAuth or a Google Cloud key. Using a third-party Apify actor, developers can batch-fetch transcripts, chunk them by character length while preserving timestamps, and prepare them for vector embedding. Key failure modes include videos with no captions at all, auto-generated versus human-authored tracks, and language provenance gaps that must be handled explicitly in the pipeline.